Structural Basis for Recognition of Cellular and Viral Ligands by NK Cell Receptors

Natural killer (NK) cells are key components of innate immune responses to tumors and viral infections. NK cell function is regulated by NK cell receptors that recognize both cellular and viral ligands, including major histocompatibility complex (MHC), MHC-like, and non-MHC molecules. These receptors include Ly49s, killer immunoglobulin-like receptors, leukocyte immunoglobulin-like receptors, and NKG2A/CD94, which bind MHC class I (MHC-I) molecules, and NKG2D, which binds MHC-I paralogs such as the stress-induced proteins MICA and ULBP. In addition, certain viruses have evolved MHC-like immunoevasins, such as UL18 and m157 from cytomegalovirus, that act as decoy ligands for NK receptors. A growing number of NK receptor–ligand interaction pairs involving non-MHC molecules have also been identified, including NKp30–B7-H6, killer cell lectin-like receptor G1–cadherin, and NKp80–AICL. Here, we describe crystal structures determined to date of NK cell receptors bound to MHC, MHC-related, and non-MHC ligands. Collectively, these structures reveal the diverse solutions that NK receptors have developed to recognize these molecules, thereby enabling the regulation of NK cytolytic activity by both host and viral ligands

An Empirical Analysis of the Impacts of the Sharing Economy Platforms on the U.S. Labor Market

Each generation of digital innovation has caused a dramatic change in the way people work. Sharing economy is the latest trend of digital innovation, and it has fundamentally changed the traditional business models. In this paper, we empirically examine the impacts of the sharing economy platforms (specifically, Uber) on the labor market in terms of labor force participation, unemployment rate, supply, and wage of low-skilled workers. Combining a data set of Uber entry time and several microdata sets, we utilize a difference-in-differences (DID) method to investigate whether the above measures before and after Uber entry are significantly different across the U.S. metropolitan areas. Our empirical findings show that sharing economy platforms such as Uber significantly decrease the unemployment rate and increase the labor force participation. We also find evidence of a shift in the supply of low skill workers and consequently a higher wage rate for such workers in the traditional industries

Estudio del mercado fotovoltaico mundial y chino

Debido a la subida del coste de la electricidad en España este año, me sentí atraído por el campo de las nuevas energías. De las muchas nuevas fuentes de energía, la solar es la que más me interesa. En primer lugar, en la parte teórica, presentaré la situación general de la industria fotovoltaica mundial, las tendencias futuras, los riesgos y cómo los científicos y economistas de todo el mundo están trabajando juntos para reducir el coste de la energía solar, aumentar la eficiencia de la generación de energía y, en última instancia, reducir el coste de compra para los consumidores con el fin de ampliar el mercado. En la parte práctica, presentaré la industria fotovoltaica en China, las tendencias futuras y los riesgos.Grado en Comerci

Culture, Conformity, and Emotional Suppression in Online Reviews

In this study, we examine consumers’ cultural background as an antecedent of online review characteristics. We theoretically propose and empirically examine the effect of cultural background (specifically individualism (versus collectivism)) on consumers’ tendency to conform to prior opinion and review texts’ emotionality. We also examine how conformity and emotionality relate to review helpfulness. We test our hypotheses using a unique dataset that combines online restaurant reviews from TripAdvisor with measures of individualism/collectivism values. We found that consumers from a collectivist culture were less likely to deviate from the average prior rating and to express emotion in their reviews. Moreover, individuals perceived those reviews that exhibited high conformity and intense emotions to be less helpful. We also present several important implications for managing online review platforms in light of these findings, which reflect the previously unidentified drivers of systematic differences in the characteristics of online reviews

Modifications and Trafficking of APP in the Pathogenesis of Alzheimer’s Disease

Alzheimer’s disease (AD), the most common neurodegenerative disorder, is the leading cause of dementia. Neuritic plaque, one of the major characteristics of AD neuropathology, mainly consists of amyloid β (Aβ) protein. Aβ is derived from amyloid precursor protein (APP) by sequential cleavages of β- and γ-secretase. Although APP upregulation can promote AD pathogenesis by facilitating Aβ production, growing evidence indicates that aberrant post-translational modifications and trafficking of APP play a pivotal role in AD pathogenesis by dysregulating APP processing and Aβ generation. In this report, we reviewed the current knowledge of APP modifications and trafficking as well as their role in APP processing. More importantly, we discussed the effect of aberrant APP modifications and trafficking on Aβ generation and the underlying mechanisms, which may provide novel strategies for drug development in AD

RRSR:Reciprocal Reference-based Image Super-Resolution with Progressive Feature Alignment and Selection

Reference-based image super-resolution (RefSR) is a promising SR branch and has shown great potential in overcoming the limitations of single image super-resolution. While previous state-of-the-art RefSR methods mainly focus on improving the efficacy and robustness of reference feature transfer, it is generally overlooked that a well reconstructed SR image should enable better SR reconstruction for its similar LR images when it is referred to as. Therefore, in this work, we propose a reciprocal learning framework that can appropriately leverage such a fact to reinforce the learning of a RefSR network. Besides, we deliberately design a progressive feature alignment and selection module for further improving the RefSR task. The newly proposed module aligns reference-input images at multi-scale feature spaces and performs reference-aware feature selection in a progressive manner, thus more precise reference features can be transferred into the input features and the network capability is enhanced. Our reciprocal learning paradigm is model-agnostic and it can be applied to arbitrary RefSR models. We empirically show that multiple recent state-of-the-art RefSR models can be consistently improved with our reciprocal learning paradigm. Furthermore, our proposed model together with the reciprocal learning strategy sets new state-of-the-art performances on multiple benchmarks.Comment: 8 figures, 17 page